In the interest of using an incremental left-to-right decoder, we restrict the contextual features to be from the preceding context only.

a successful dialog system relies on the synergistic working of several components: speech recognition (aSR), spoken language understanding (SLU), dialog management (DM), language generation (LG) and text-to-speech synthesis (TTS).


The combined training objective (2) exploits unlabeled data to improve the CRF model, as we will see.

nevertheless, the algorithm is sufficiently efficient to be trained on unlabeled data sets that yield a notable improvement in classification accuracy over standard supervised training.


These training criteria have yielded excellent results for various tasks.

sequential segmentation tasks (ssTs), such as text chunking (Chunking) and named entity recognition (NER), which constitute the shared tasks of the Conference of Natural Language Learning (CoNLL) 2000, 2002, are typical CRF applications.


We first provided a general framework of CRF training based on MCE criterion.

initial parameters can affect the optimization results, since QuickProp as well as L-BFGS can only find local optima.


Comparing the algorithms in Figures 1 and 2, we notice three differences between BLasso and Boosting: (i) the use of backward steps in BLasso; (ii) BLasso uses the grid search (fixed step size) for feature selection in Equation 12while Boosting uses the continuous search (optimal step size) in Equation 7; and (iii) BLasso uses a fixed step size for feature update in Equation 13while Boosting uses an optimal step size in Equation (8).

f-Boosting was implemented as figure 2, by setting a large value to θ in Equation (15), i.e., θ = 10 3 , to prohibit backward steps.


More importantly for our purposes, directly minimizing lasso function of Equation 10with respect to λ is not possible when a very large number of model parameters are employed, as in our task of LM for IME.

this paper studies LM on the application of Asian language (e.g.


From this, we can say that the headword bigram features show moderate to high degree of correlation with the word bigram features.

the solution to the lasso problem for a general convex loss function and an adaptive α remains open.


BETSY requires the Bayesian rules, and Intelli-Metric, the AI-based rules.

when analyzing content, a mechanism is needed for automatically selecting a term-document cooccurrence matrix in accordance with the essay targeted for evaluation.


This is a kind of negative halo effect: create a bad impression, and you will be scored low on everything.

most of these factors are present in giving tests, and the human rater, in particular, is a major error factor in the scoring of essays.


The Bayesian Essay Test Scoring sYstem (BETSY) is a windows-based program that classifies text based on trained material.

bennet and bejar (1998) have criticized the overreliance on human ratings as the sole criterion for evaluating computer performance because ratings are typically based as a constructed rubric that may ultimately achieve acceptable reliability at the cost of validity.


In this paper, we apply essentially the same techniques to data-driven dependency parsing, specifically targeting the analysis of coordination and verb groups, two very common constructions that pose special problems for dependency-based approaches.

nivre and nilsson (2005) improve parsing accuracy for MaltParser by projectivizing training data and applying an inverse transformation to the output of the parser, while Hall and novák (2005) apply post-processing to the output of Charniak's parser (Charniak, 2000).


For example ample and plentiful refer to the portion size, fancy may refer to the presentation, and delicious describes the flavors.

these indicate relations not in the ontology.


1.4 Table 10: The 14 utterances with consistency scores below 3. ical proofs.

the results show that: (1) the learned mappings provide good coverage of the domain ontology and exhibit good linguistic variation; (2) the consistency between the semantic representations and realizations is high; and (3) the naturalness of the realizations are significantly higher than the baseline.


Our procedure for deriving semantic representations is based on the hypothesis that if U contains named-entities that realize the distinguished attributes, that R will include the relevant relation in the domain ontology.

there is a trend for mappings to be rated higher for the food attribute (df=1, F=3.14, p=0.08) and the value attribute (df=1, F=3.55, p=0.06) for consistency, suggesting that perhaps it is easier to learn some mappings than others.


Also, as our data usually offers no frequency information, we have adopted the maximum entropy assumption, namely, that all relative frequencies are equal.

the second asks how to measure distance given these aspects.


A further difficulty lies in the fact that many languages are not represented by the full 200 meanings.

in this tree, Ossetic is placed as an outlier of the Indian group, while its traditional classification (and the Rao distance tree) puts it among the Iranian languages.


Synonymy, hypernymy or meronymy fall clearly in this latter category, and well known resources like WordNet (Miller, 1995), EuroWordNet (Vossen, 1998) or MindNet (Richardson et al., 1998) contain them.

we consider that this occurs when for a given text segment, no word can be selected as a representative of the topic of the segment.


Many Information Extraction papers discover relationships between words using syntactic patterns (Riloff and Jones, 1999).

we are not aware of work in this direction that has been evaluated with good results on lexical category acquisition.


This claim is supported by the experiments in Sections 5 and 6.

berland and Charniak (1999) suggest their work may be useful for building a lexicon or ontology, like WordNet.


The POS tagger and PP attacher capture lexicalized regularities which are genuinely new to the grammar: in effect, they refine the language model of the grammar in places that would be tedious to describe through individual rules.

where possible, we re-used publicly available components to make the predictions rather than construct the best predictors possible; it is likely that better predictors could be found, but components 'off the shelf' or written in the simplest workable way proved enough to demonstrate a positive benefit of the technique in each case.


The output of the retrained TnT then predicts the label of each word, whether it follows or precedes its regent, and what other types of relations are found below it.

a complete search usually requires more time and space than available, and often fails to return a usable result at all.


Indeed, the confidence we can have in the estimation S   ; if it is low, it will be closer from S: In these experiments, we used the smoothing function λ(|O f |) = 1 − e −β|O f | with β = 0.1.

first of all, the visualization environment can be enhanced, as is the case for the implementation of the algorithm itself.


A user who wants to concentrate on almost certain errors rather than on most frequent ones can visualize suspicious forms ranked according to M f = S f .

we then define the mean suspicion rate S f of a form f as the mean of all suspicion rates of its occurrences: To compute these rates, we use a fix-point algorithm by iterating a certain amount of times the following computations.


At first blush one might think that gathering even more fine-grained features from a WSJ treebank would not help adaptation.

while this is a coherent position, it is far from the majority view.


annotated data (or a comprehensive list of verb senses) exists for the domain.

from the other methods we requested K = 2 to 60 clusters.


Although subjects, objects, and their combination are widely used contextual information, the performances for subj and obj categories, as well as their combination subj+obj, were relatively poor.

because nouns are the main target of synonym acquisition, here we limit the target of acquisition to nouns, and firstly extract the cooccurrences between nouns and three categories of contextual information -dependency, sentence co-occurrence, and proximity -from each of three different corpora, and the performance of individual categories and their combinations are evaluated.


Events can be broadly defined as "Who did What to Whom When and Where".

existing work has typically been based on techniques that extract key textual elements, such as keywords (also known as significant terms) as weighed by their tf*idf score, or concepts (such as events or entities) with linguistic and/or statistical analysis.


The study was conducted remotely over the Internet.

the Fscore correlates significantly with human ratings, yielding a correlation coefficient of r = 0.575 on the Ziff-Davis corpus and r = 0.532 on the Broadcast news.


From each pair of segments, we generate one positive and one negative training instance as follows.

the correlation is measured by three metrics, Spearman's rank correlation, Kendall's rank correlation, and average continuity (described later).


The main reason is that the understanding of the basic entailment processes will allow us to model more accurate semantic theories of natural languages (Chierchia and McConnell-Ginet, 2001) and design important applications (Dagan and Glickman, 2004), e.g., Question Answering and Information Extraction.

they were correctly classified by our overall model (last column) and miss-classified by the models in the seventh and in the eighth columns.


In Section 5, we describe the evaluation of the algorithm on the Rondane corpus.

this means we can remove eliminable splits from a chart repeatedly, i.e.


As the figure shows, the asymptotic runtimes for computing the complete chart and the reduced chart are about the same, whereas the time for enumerating all configurations grows much faster.

even first-order equivalence is an undecidable problem, and broadcoverage semantic representations such as those computed by the ERG usually have no welldefined model-theoretic semantics and therefore no concept of semantic equivalence.


The results are reported in the 'Fixed Coverage' column of Table 2.

most work on syntactic priming has been concerned with sentence production; however, recent studies also demonstrate a preference for structural repetition in human parsing.


When this happens, we do not count the distance between the first and second expansion.

there are various approaches to modeling processing difficulty using a probabilistic approach.


The DTree classifier achieved 81.6% LR and 83.6% LP.

we present a novel classifier-based deterministic parser for Chinese constituency parsing.


But compared to all other works, our classifier stacking model gave better or equal results for all three measures.

the MBL model did not perform well; although MBL and SVM differed in accuracy by only about 3 percent, the parsing results showed a difference of more than 10 percent.


In terms of speed and accuracy trade-off, there is a 5.5% trade-off in F1 (relative to SVM's F1) for a roughly 14 times speed-up between SVM and two-stage DTree.

we did not find metaclassification and simple voting very effective.


Inspecting a large grammar by hand is difficult, but fortunately, our baseline grammar has less than 100 nonterminal symbols, and even our most complicated grammar has only 1043 total (sub)symbols.

in our case, the variance is higher for models with few subcategories; because not all dependencies can be expressed with the limited number of subcategories, the results vary depending on which one EM selects first.


Since A now combines the statistics of A 1 and A 2 , its production probabilities are the sum of those of A 1 and A 2 , weighted by their relative frequency p 1 and p 2 in the training data.

for example, the symbol NP might be split into the subsymbol NPˆS in subject position and the subsymbol NPˆVP in object position.


The new parallel corpus is a small one, approximately 1.5 million words, but contains texts from different domains: magazine articles, modern fiction, texts from international organizations and academic textbooks.

for some experiments MB did better, for others BB was the method that improved the performance; nonetheless for some combinations MB together with BB was the method that worked best.


Furthermore, such approaches circumvent the sisyphean need for specifying explicitly a set of stipulated senses.

in its original unsupervised version, the only resource required by the algorithm is a machine readable dictionary with one definition for each possible word sense.


Then, we exploit such model to match the contexts of the target word, providing a decision criterion for sense matching.

this was done by creating a list of all WordNet synonyms of the target word, under all its possible senses, and picking randomly one of the synonyms as the source word.


The contribution of the non-local information introduced by our method was not significant in the experiments.

we divided the original training set into 1800 abstracts and 200 abstracts, and the former was used as the training data and the latter as the development data.


As reported in Table (5), 1797 pairs of parallel documents mined by the new scheme is not covered by the URL pattern-based scheme.

based on these mining results, the quality of the mined data, the mining coverage and mining efficiency are measured.


3 We use equal amounts of data from each source so as not to bias the corpus to either data source.

we performed ablation experiments to investigate the effect of varying the amount of question and non-question training data on the parser's performance.


In Sections 02-21 of the Penn-II Treebank, these are used more often in relative clause constructions than in questions.

the 5500 questions were stripped of answer type annotation, duplicated tREC questions were removed and 2000 questions were used for the question treebank.


Alternatively, the resource can be used as a stand-alone training corpus to train a parser specifically for questions.

the results for the tests on WSJ Section 23 are considerably worse.


The relative pronoun is a dative object (edge label DA) of the embedded infinitive, and is therefore attached at the VP level.

formalisms such as CCG and TAG are particularly suited to capture the cross-ing dependencies that arise in languages such as Dutch or German, and by choosing an appropriate linguistic representation, some of these problems may be mitigated.


To differentiate such alignments, we introduce a feature that sums the differences between the distance between consecutive aligned source words and the distance between the closest target words they are aligned to.

this motivates the distinction between the d 1 and d >1 parameters in IBM Models 4 and 5.


From the best derivation, the best English e * is produced.

this template-based scheme not only captures the reorderings of phrases, but also integrates some phrasal generalizations into the global model.


Compared with word-based SMT systems, phrase-based systems can easily address reorderings of words within phrases.

each corner has four links in four directions: topright, topleft, bottomright, bottomleft, and each link links a set of blocks which have the corner as their vertex.


The distortion cost is added to the log-linear mixture of the hypothesis extension in a fashion similar to the language model cost.

in our experiments we show that the language model is not reliable enough to make the choices that lead to a better MT quality.


So, as an addition to the second issue stated in the previous subsection, a bigram feature might even cover for more than one word-bigram.

in the previous section, only the intraword bigrams were discussed together with the words.


In fact, comparable corpora are easier to collect for most languages (e.g.

most of the named entities, such as microsoft and HIV are expressed using the same string in both languages.


The English and the Italian corpora are comparable, in the sense stated in Section 2, i.e.

we evaluate the performance of the cross-lingual text categorization, using both the Bow Kernel and the Multilingual Domain Kernel, observing that also in this case the leverage of the external knowledge brought by the MDM is effective.


Because the edit region identification results on the original Switchboard are not directly comparable with the results on the newly segmented data, the state-of-art results reported by Charniak and Johnson (2001) and Johnson and Charniak (2004) are repeated on this new corpus by .

the functions split() and merge() are used to split and merge the feature space respectively.


Since the gains need to be positive to proceed the process, the difference between the Likelihood of the current and the ML value becomes smaller and smaller.

before presenting the PFS algorithm, we first give a brief review of the conditional maximum entropy modeling, its training process, and the SGC algorithm.


We couldn't use the new features with the SGC algorithm either for the same reason.

using CME instead of boosting incurs a performance hit.


Thirdly, they do not compute the relevance of search results and the relativity of expanded terms in an iterative fashion.

here, we adopt a similar quantitative evaluation as what Xuehua Shen et al.


For each of these actions,  The Iterative Algorithm module implements the HITS-like algorithm described in section 2.

the query expansion method, which can provide fresh and relevant documents, can help the re-ranking method to reach an even better performance.


One explanation for this is that the more implicit feedback information generated, the more representative terms can be obtained, and thus, the iterative algorithm can perform better, leading to more precise search results.

(J. Pitkow et al., 2002) to the best of our knowledge, how to exploit these two types of implicit feedback in a unified way, which not only brings collaboration between query expansion and result re-ranking but also makes the whole system more concise, has so far not been well studied in the previous work.


The other factor is the translation resources comprised in the MT system.

at the fifth NTCIR workshop, almost all the groups participating in Bilingual CLIR and Multilingual CLIR tasks adopt the query translation method using MT systems or machine-readable dictionaries (Kishida et al., 2005).


We assume that each subtreeT k produces a target stringS k .

here, I is the length of the target string, and J is the length of the source string.


For Lynx, seven features described in section 2 were used.

the TAT-based translation model can be decomposed into four sub-models: 3.


With a large t o , OTHER becomes more desirable than the other NE classes, and many words are classified as non-NE words and vice versa.

note that nER recall with ASR results could not exceed the rate of the remaining nEs after ASR (about 82%) because nEs containing ASR errors were always lost.


In experiments using SVMs, the proposed method showed a higher NER Fmeasure, especially in terms of improving precision, than simply applying text-based NER to ASR results.

in text-based NER, better results are obtained using discriminative schemes such as maximum entropy (ME) models (Borthwick, 1999;Chieu and Ng, 2003), support vector machines (SVMs) (Isozaki and Kazawa, 2002), and conditional random fields (CRFs) (McCallum and Li, 2003).


That is, the likelihood of È´Û ½ Û Ý µ is expanded to Since the coefficients of Equation (4) were determined arbitrarily (Kang, 2004), they are replaced with parameters « of which values are determined using a held-out data.

the best sequence of word spacing for the sentence is defined as by where AE is a sentence length.


Such exceptions induce many mistakes of word spacing even in news articles.

otherwise, ChangingWindowSize checks if the window size should be shrinked by calling HowSmall-Shrink.


The main drawback of Ò-gram model is that it suffers from data sparseness however large the corpus is.

the state-of-the-art performance on Korean word spacing is to use the hidden Markov model.


Simultaneously the computational cost for´Ò • ½ µ -grams is far higher than that for Ò-grams.

the proposed method automatically adapts the window size Ò.


", it means that my father entered the bag, which is totally different from the original meaning.

the goal of this paper is to provide a new method for processing automatic word spacing with an Ò-gram model.


That is, if shrinking is applied ahead of expanding, 5,512 additional errors are made.

if both functions return zero, the window size should not be changed.


Since English abbreviations frequently appear in Korean and Chinese texts, such as "세계무역기구 (WTO)" in Korean, "世界贸易 组织 (WTO)" in Chinese, it is essential in IR to have a mapping between these English abbreviations and the corresponding words.

the semantic weight is calculated from the bilingual dictionary.


This is along the line of work where researchers attempt to index documents with concepts rather than words.

the mixed use of English and local languages presents a classical problem of vocabulary mismatch in monolingual information retrieval (MIR).


For example, if we set n to 4 and m to 2, the window " 1 2 3 4 w w w w " consists of four words.

although the technique we developed has values in their own right and can be applied for other language engineering fields such as query translation for CLIR, we intend to understand to what extent monolingual information retrieval effectiveness can be increased when relevant terms in different language are treated as one unit while indexing.


Since the available corpus size is rather small we could not compare the results obtained with various other word alignment algorithms proposed in the literature.

noun, verb, adjective) that are significant from the alignment point of view.


Table 3 shows the precision and recall of each rule.

we verify the effectiveness of our methods experimentally.


Results on a large scale evaluation indicate that this learning improves disambiguation for complex tag sets.

to English tag sets whose sizes range from 48 to 195, the number of tags for Hebrew, based on all combinations of the morphological attributes (part-of-speech, gender, number, person, tense, status, and the affixes' properties 2 ), can grow theoretically to about 300,000 tags.


Upon reflection, it is not surprising that a unigram language model would segment words in this way.

we performed two experiments suggesting that our own inference procedure does not suffer from similar problems.


The goal of the evaluation is primarily to investigate how reduced lexical resources affect the performance of morphological analysis, as we will not have complete lexicons for the dialects.

++¤ ) '[imperfective subject 2nd person fem.


Morphological Behavior Classes: The MBCs are variant-independent, so in theory no changes needed to be implemented.

we use LATB files fsa 16* for development, and for testing, files fsa 17*, fsa 18* (14 conversations, 3,175 distinct verbal types).


Our implementation uses the multitape approach of Kiraz (2000).

our quantitative analysis in Section 8.3 leaves open the question of how good the gold standard is in the first place.


Much less significant errors were cases of extra Adverbs or Adjectives at the end of the chunk, and missing adverbs before or after the chunk.

we do not have a good explanation for this observation -but we are currently verifying the consistency of the gender annotation in the corpus (in particular, the effect of the unmarked gender tag).


We have also shown that using morphological features enhances chunking accuracy.

to evaluate the applicability of this definition to Hebrew, we tested this definition on the Hebrew treeBank (Sima'an et al, 2001) published by the Hebrew Knowledge Center.


Note that, even when using the probabilities as features, only a small amount of additional POS ambiguity is required to significantly improve performance.

a log-linear model is used to score the alternative analyses.


Clark and Curran (2004a) propose a simple method for calculating P (C i = c|S): use the word and POS features in the local context to calculate the probability and ignore the previously assigned categories (the history).

although POS tagging accuracy seems high, maintaining some POS tag ambiguity in the language processing pipeline results in more accurate CCG supertagging.


For another example, there is a part-of-speech named sahen-noun (verbal noun) in Japanese.

models in which the whole document is taken into consideration need a lot of computation compared to models with only local features.


titles of persons), this will give useful clues for guessing the part-of-speech of the ambiguous one, because unknown words with the same lexical form usually have the same part-of-speech.

this method uses unlabeled data in only the testing phase, and the training phase is the same as the case with no unlabeled data.


We treat all N-best phrase alignments equally.

the accuracy of "lex" drops rapidly as the number of conditioning factors increases.


In the active learning setting, as long as we do well on the actively selected samples, we do well on the whole sample space.

§ f © ¡ © ): is a threevalued orientation component linked to the block g : a block is generated to the left or the right of its predecessor block g w , where the orientation ¡ g i w of the predecessor block is ignored.


6 to update the relevant set, in reality, this idea is difficult to implement because it requires a more costly decoding step.

about million Arabic-English phrase pairs for the experiments in this paper.


A theoretical justification for the novel training procedure is given in Section 3.

in Table 2, we adopt an approximation, where the relevant set is updated by adding the decoder output at each stage.


The notion of word may be necessary for the user's facility.

word Ò-gram probability estimated from an SSC may not be as accurate as an LM estimated from a corpus segmented appropriately by hand.


the candidate evaluation metric of a phoneme-to-text transcription (Mori et al., 1999) composed of the word Ò-gram model and the word-based pronunciation model is as follows: To cope with segmentation errors, the concept of stochastic segmentation is proposed (Mori and Takuma, 2004).


We applied various feature engineering techniques to combat the noise introduced by the speech recognition system and applied text clustering techniques to group topically similar calls together.

the documents contain well formed sentences which allow for parsers to be used.


These results suggest the time is ripe for exploiting 'imperfect' features in our machine learning approach.

likewise, time expressions are flagged and their values normalized, based on TIMEX3, an extension of the ACE (2004) (tern.mitre.org) TIMEX2 annotation scheme.


We can therefore never be entirely sure whether or not a proposed output is safe to update towards.

figure 2 shows the space of translations schematically.


The output of the algorithm is new hypothesized alignments of the training corpus.

there are tasks with large numbers of parameters where there are sufficient labels.


With rules 10 and 11, the non-terminal X 1 behaves as a place holder where certain number of foreign words are skipped.

• A stack that keeps track of the uncovered foreign word spans.


The stack for an initial hypothesis is initialized with span [1, J].

it cannot hypothesize a long-distance reordering required for linguistically divergent language pairs.


In this paper, we propose a measure of similarity to capture this intuition.

in this paper, we propose a measure of similarity to capture this intuition.


Without the log terms, the highest scoring conceptual instances are too generic (i.e., they are too high up in the ontology).

since there is little high precision sense-tagged corpora, methods are required to ontologize semantic resources without fully disambiguating text.


3 We use the notation C mn ij to denote that i and j are (m, n)-cousins.

for each proposed hypernym or coordinate link involved with the novel hyponym i, we compute the set of candidate hypernyms for i; in practice we consider all senses of the immediate hypernym j for each potential novel hypernym, and all senses of the coordinate term k and its first two hypernym ancestors for each potential coordinate.


The intuition behind our criteria for ranking generalized pattern is that patterns of higher precision tend to contain words that are indicative of the relation being mined.

for each window, the word with the highest frequency score, as computed in the first pass of the procedure for scoring the patterns, determines the score of the candidate fact in that context.


Time sequence scoring is then used to rerank the list and choose the candidate best temporally aligned with the NE.


In order to score a pair of entities across languages, we compute the similarity of their time distributions.

in order to rely on the time sequences we obtain, we need to be able to group variants of the same NE into an equivalence class, and collect their aggregate mention counts.


Moreover, the in-between word features and the entity-related features used in the feature-based methods are also captured by the tree kernel and the entity kernel, respectively.

finally, it is worth noting that by introducing more individual kernels our method can easily scale to cover more features from a multitude of sources (e.g.


To some degree, it could possibly lead to over-fitting and compromise the per-formance.

it also shows without the need for extensive feature engineering the composite kernel can not only capture most of the flat features used in the previous work but also exploit the useful syntactic structure features effectively.


The Japanese language is basically an SOV language, but word order is relatively free.

statistical Japanese dependency analyzers (Uchimoto et al., 2000;Kudo and Matsumoto, 2002), however, will output the result where "keisatsu-de" modifies "arui-teiru".


It thus pays off to not only use subtrees rather than substrings (as in CCM) but to also reestimate the subtrees' probabilities by a maximum-likelihood procedure rather than using their (smoothed) relative frequencies (as in U-DOP).

subtrees, from this corpus, by means of a node-substitution operation indicated as °.


A possible drawback of U-DOP, however, is the statistical inconsistency of its estimator (Johnson 2002) which is inherited from the DOP1 model (Bod 1998).

here the appropriate t' is defined to be t f − t. A state s t is connected backward to all states s tb such that t = t b ° t', for some t' .


We choose a single correct path from each training parse tree, and the training examples correspond to all candidate inferences considered in every state along this path.

parallelization uniformly reduces memory usage because each label classifier trains only on inferences whose consequent item has that label.


The local factor for a binary rule r = X → Y Z, over span (i, j), with CCM features F ij = (y ij , c ij ) and prototype feature p ij , is given by the product of local factors for the CCM and CFG+ models: From these local factors, the inside-outside algorithm produces expected counts for each binary rule, r, over each span (i, j) and split point k, denoted by P (r, (i, j), k|S, F, F ).

clearly, we need to do something more than adding structural bias (e.g.


The CCM and PCFG models provide complementary views of syntactic structure.

human-elicited prototypes are not likely to have the property that, when they occur, they are (nearly) always constituents.


The Theme argument is lost along this relation, instead the new argument with the thematic role of Agent receives the special value ANY-CONCEPT.

the classes are organized hierarchically.


This similarity can be computed by comparing the target argument structure with the propagated argument structure.

the type of the start structure can be different than the type of the target structure.


When TE was used to filter non-entailed answers from consideration (Method 1), the overall accuracy of the Q/A system increased by 12% over the baseline (when an EAT could be identified) and by nearly 9% (when no EAT could be identified).

under these approaches (referred to as licensing by (Groenendijk, 1999) and aboutness by (Lewis, 1988)), p is considered to be an answer to a question ?q iff ?q logically entails the set of worlds in which p is true(i.e.


Productions: Figure 2: An example of an NL query and its MR in a functional query language with its parse tree.

kRISP learns a semantic parser iteratively, each iteration improving upon the parser learned in the previous iteration.


We compared our system's performance with the following existing systems: the string and tree versions of SILT , a system that learns transformation rules relating NL phrases to MRL expressions; WASP (Wong and Mooney, 2006), a system that learns transformation rules using statistical machine translation techniques; SCISSOR (Ge and Mooney, 2005), a system that learns an integrated syntactic-semantic parser; and CHILL (Tang and Mooney, 2001) an ILP-based semantic parser.

we note that SCISSOR requires more supervision for the training corpus in the form of semantically annotated syntactic parse trees for the training sentences.


The baseline for experiment B (continuation-type) were randomly selected sentences containing citations, because they often co-occur with statements of continuation.

"we adopt the approach presented in [1].


The examples associated with the VN lexicon are extracted from the PB corpus.

in this paper we have investigated the above claim by designing a semi-automatic algorithm that assigns iLCs to FN verb predicates and by carrying out several semantic role labeling (SRL) experiments in which we replace the frame with the iLC information.


The platform provides dictionary access and edition services without any new and specific development.

2 Jibiki, The Papillon Dictionary Development Platform The Jibiki platform has been designed to support the collaborative development of multilingual dictionaries.


The expected behaviour may only be achieved if : • the system knows in which volume the search is to be performed, • the system knows where, in the volume entry, the headword is to be found, • the system is able to produce a presentation for the retrieved XML structures.

in order to concretely address these problems, several institutions representing translators, terminologists, legal experts and computational linguists joined in the LexALP project, co-funded by EU's iNTERREG iiib Alpine Space programme.


Some keyword phrases contain words with high translation value, but the keyword phrase itself has low thesaurus value.

it first scans the English input to find the longest matching substring in our dictionary, and replaces it with the most likely Czech translation.


The hierarchical arrangement of keyword phrases presents another issue: some concepts, while not of great value for access to segments of video, may be important for organizing other concepts and for browsing the hierarchy.

we fully expect these results to hold across larger samples and for more sophisticated translation systems.


We present a process of prioritized translation that balances the issues discussed above.

we prioritized the order of keyword phrase translations based on the translation value of the untranslated words in each keyword phrase.


The aligner linked two words to each other only if neither of them was on the function word list and their longest common subsequence ratio (Melamed, 1995) was at least 0.75.

the experiments in Section 3 show that more complex patterns occur surprisingly often even in highly reliable alignments in relatively simple bitexts.


We assume that the phonetic mapping models depend merely on the current observation.

but in fact, pronunciations of the two initials are very close to each other in Chinese speech.


On the other hand, wed-based chat rooms and BBS systems are often abused by solicitors of terrorism, pornography and crime (McCullagh, 2004).

to address the sparse data problem and dynamic problem in Chinese chat text normalization, the phonetic mapping models are proposed in this paper to represent mappings between chat terms and standard words.


Thus, the misclassification function in equation 8does not represent the likelihood that S will be incorrectly segmented.

according to equation 5and 8, we can write the misclassification function using Г B and Г′ separately:  Where denotes the number of times the bigram w x w y appears in sequence .


Sort the bigrams according to their "importance"; 4.

it is expected that models produced by step-by-step growing algorithm may achieve better performance than growing algorithm, and smaller step size will lead to even better performance.


From the results we can see that after the number of training samples reaches 600 there are only subtle changes in accuracy and recall.

in all the experiments we used the standard viterbi algorithm to search for the best output of source channel model.


These characteristics are indicative of salient objects and empirical results support this correlation between object salience and landmark selection (Beun and Cremers, 1998).

moreover, in this scene it is the only object that can function as a landmark without recourse to using the scene itself or a grouping of objects.


The case markers that can appear followed by wa are indicated by a check mark in the column "+wa" in Table 1.

but how reliably can we predict case markers in Japanese using the information that exists only in the sentence?


Considering only the 16-word subset with both S and O senses (according to Judge 1), κ is .75, and for the 16-word subset for which Judge 1 gave only S or only O senses, κ is .73.

table 1 shows the contingency table for the two annotators' judgments on this data.


Note that the annotations in the MPQA corpus are for subjective expressions in context.

boil -(come to the boiling point and change from a liquid to vapor; "Water boils at 100 degrees Celsius") => change state, turn -(undergo a transformation or a change of position or action; "We turned from Socialism to Capitalism"; "The people turned against the President when he stole the election") He sold his catch at the market.


There are several other ways our results could impact future work.

classifying a sense as O means that, when the sense is used in a text or conversation, we do not expect it to express subjectivity and, if the phrase or sentence containing it is subjective, the subjectivity is due to something else.


We can invert the question by substituting the candidate answer back into the question, while making one of the given entities the socalled wh-word, thus "Of what country is Paris the capital?"

the Constraints work reported here has highlighted the need for more extensive equivalence testing.


At the same time, all of the four systems improve the performance against the corresponding system without QE.

we estimate λ by maximizing the likelihood of all training instances, given the bigram or biterm model: BP and P(t 1 ) are ignored because they do not affect λ .


Let A be a feature structure with no reentrancies.

a sequence of feature structures, where some nodes may be shared by more than one element, is a multi-rooted structure (MRS).


We believe that it should be possible to optimize the mapping such that much smaller grammars are generated.

when rules of a one-reentrant unification grammar are mapped to LIG rules (definition 14), FH and UH are used to define heads of fixed and unbounded LIG rules, respectively.


We leave the formal details of such an extension to future work.

we are looking into mappings of one-reentrant UGs to other MCSL formalisms, notably TAG.


note that a weighted sum of these costs could be used to directly rerank realizations, in much the same way as relative frequencies and concatenation costs are used in (Bulyko and Ostendorf, 2002); in our experiments, we let SVM light determine how to weight these costs.


Note that a weighted sum of these costs could be used to directly rerank realizations, in much the same way as relative frequencies and concatenation costs are used in (Bulyko and Ostendorf, 2002); in our experiments, we let SVM light determine how to weight these costs.

since COSTS does significantly improve upon NGRAM2 (t a Q:S, p < H:HHI), there is some value to the use of synthesizer features in the absence of WORDS.


Intuitively, E-C pairs of low frequency indicate uncommon events which are of higher interest to the model.

the task of labeling samples is basically to distinguish the qualified transliteration pairs from the rest.


It reinforces the knowledge that PSM has acquired and allows PSM to adapt to changes at no cost.

they often skew the transliterations in different ways to create special meanings to the sound equivalents, resulting in casual transliterations.


Note that the last two long vowels are also used as consonants.

so PMT system fulfills the requirement of transliteration across two scripts of Punjabi.


Section 4 describes the system we compare our approach with, as well as the experiments and the results.

as result of this step a list of corresponding bilingual cognates is obtained.


Although word-sense disambiguation methods can be applied, these are not free of errors.

in our case we used a corpus with morphosyntactical annotations and the NEs identified and classified with the FreeLing tool (Carreras et al., 2004).


The accuracies are calculated with the equation: .

in addition, since we obtained a high recall (0.969), many of the noisy sentences will be filtered out at this step and the classifier of the second step is likely to perform well.


We also propose to use Support Vector Machines to filter out noisy instances that indicates no specific time period.

to realize our goal, we focus on expressions associated with time-slot (time-associated words).


They use the handcrafted dictionary and some inference rules to determine the time periods of events.

such circumstances have evoked many studies on information extraction from text especially on the internet, such as sentiment analysis and extraction of location information.


The features in CONTEXT would be effective for estimating time-slot of the sentences as mentioned in Ex-ample2 in Section 3.1.

these expressions are rare in usual text.


PerfMatch, on the other hand, cannot handle one-to-many relationships, acts "cautiously" and aligns the English NP to a dummy node, leading to a recall error.

first, a real-valued function sim : U s × U t → R estimates pairwise similarities between source and target units.


Given a relation type for acquisition, we first acquire candidate predicate pairs that are likely to be of the given relation exploiting a state-of-the-art pattern-based method.

an action-means relation, on the other hand, holds between events x and y if and only if volitional action y is likely to be done as a part/means of volitional action x.


They also showed that using the source corpus allowed to significantly improve results when only 10%-30% of the target corpus was used for training.

section 3 presents the datasets used, and section 4 the learning methods, including the application of sVD.


The multinomial model assumes that documents are the result of repeated trials, where on each trial a word is selected at random, and the probability of selecting the j-th word from class c is θ cj .

this leads us to suggest estimates for parameters such that the expected value of the distribution is as in 10 but with the θ ij (which are unknown) replaced with their maximum likelihood estimates, θ ij .


In many cases, the first (and often only) model is one in which counts of words are modelled as binomial-or Poissondistributed random variables.

generative probabilistic classifiers, often represented by the multinomial classifier, have in these same studies performed poorly, and this empirical evidence has been bolstered by theoretical arguments (Lasserre et al., 2006).


Conventional parameter estimates are affected (in some way or other) by the likelihood function for a parameter, and the likelihood function is such that longer documents exert a greater influence on the overall likelihood for a parameter.

given the inflexibility of the Dirichlet multinomial model, we argue that such a sacrifice is justified.


The presence or absence of a cue is not in itself sufficient to determine whether the language is deceptive or truthful.

pre-trial interviews where investigators are looking for leads.


The eigenvalue decomposition of B now incorporates this extra term information provided in D 1 , and the eigenvectors show stronger correspondence between those terms indicated.

this information may be encoded in the diagonal block D 1 by replacing zeroes in the cells for (house, maison) and its symmetric entry with some nonzero value indicating the strength of association for the two terms.


As with many problems, there is pressing need to find strategies for reducing the amount of supervision required for producing accurate supertaggers, but as yet, no one has explored the use of weak supervision for the task.

dirichlet priors can be used to bias HMMs toward more skewed distributions (Goldwater and Griffiths, 2007;Johnson, 2007), which is especially useful in the weakly supervised setting considered here.


We used subject and object dependencies, as well as adjective and noun modifier dependencies.

we are interested to find out whether we outperform Mc-Carthy et al.


Parse trees were merged into forests by grouping constituents of the same type (for example, the two NPs and two VPs are grouped).

the choice of using the first translator as the key was arbitrary.


n-grams it's 5 minutes, 5 minutes on, …… on foot ., about 5 minutes …… 5 minutes .

it can produce alternative translations which contain word re-orderings and phrase structures not considered by the search algorithm of the decoder (Chen, et al., 2007).


As a pattern could be reduced by different seed pairs, we define the occurrence frequency of a pattern as the sum of the counts of the pattern for all the seed pairs, using following formula: where is the frequency of pattern ; is a seed pair; is the set of all seed pairs.

during training, for each other-anaphor, we consider as the candidate antecedents the preceding NPs in its current and previous two sentences.


An other-anaphor is said to be covered by a pattern set, if the anaphor and its antecedent could be hit (i.e., the corresponding query has a non-zero hit number) by at least one pattern in the list.

for example, in "...asthma and other hypereosinophilic diseases", the reference between "other hypereosinophilic diseases" and "asthma" is clear if the inbetween words "and other" are taken into consideration.


Substantially all of the Bible, in particular, is already electronically available in at least 40-50 languages from diverse language families (Biola University 2005-2006).

this is demonstrated in Chew and Abdelali (2008).


We would like to explore, for example, whether it is possible to use tensor decomposition methods like PARAFAC2 to leverage associations between n-grams, words, documents and languages to still better advantage.

for 'comingle', co+mingle, coming+le, comingle, c+o+m+i+n+g+l+e, etc., are some of the candidates, but comi+ngl and com+mingle are not.


(2004) present an end-to-end paraphrasing system inspired by phrase-based machine translation that can both acquire paraphrases and use them to generate new strings.

o: The scheme was intended for people of poor or moderate means.


This paper presents an implemented hybrid approach to grammar and style checking, combining an industrial patternbased grammar and style checker with bidirectional, large-scale HPSG grammars for German and English.

the combination of shallow and deep components, hybrid processing, has already been investigated in several modular architectures, such as GAtE (Gaizauskas et al., 1996), Whiteboard (Crysmann et al., 2002) and Heart-of-Gold (Callmeier et al., 2004).


The generator and HPSG grammars we use are able to provide a range of realisations for a given semantic input.

if there is one and this is not ranked as the best parse, it is moved to the first position in the parse list.


If there is one and this is not ranked as the best parse, it is moved to the first position in the parse list.

for English, MAL-rules for errors concerning subject verb agreement and missing determiners were implemented.


This suggests that the frequency effect is not so strong as to over-  Table 9: Confusion matrix for L1 determiners ride any true linguistic information the model has acquired, otherwise the predominant choice would always be the null case.

this is largely driven by the expanding population of L2 English speakers, whose varying levels of ability may require different types of NLP tools from those designed primarily for native speakers of the language.


More precisely, we generate all possible combinations of trouble expression and objects and rank them according to the following score.

if you observe "cannot go to Y (by / because of) X" and X is not a trouble expression, then you can expect to find "can go to Y (by / because of) X" as well.


There is substantial error reduction for low-frequency verbs (<21 PTB occurrences).

grammars used previously have not been treebank grammars (for e.g., Carroll and Rooth (1998) and Beil et al.


We investigate the WSJ portion of the Penn Treebank, and, to provide more robust evaluation, also compare the TIGER corpus of German, version 2 (Brants et al., 2002) where appropriate.

even though the abstraction to no information identifies more word tokens, the ambiguity class abstraction correctly categorizes nearly as many words.


The variation all CC/RB disappeared points to an error in the word but, yet there is also a noticeable inconsistency in the word all.

indeed, there is an aver-age of 2.56 categories per variation, with one variation (and in) having 21 categories.


For this, we could take all framed nuclei from a corpus and compare the level of ambiguity for differing abstractions.

in the second pass through the corpus, we break the ambiguity class down into its pairs, and each relevant pair is stored as a variation nucleus.


Table 2: Word token accuracy for the WSJ With the smaller and likely more accurately tagged TIGER corpus, we find exactly the same trends, as shown in table 3.

when abstracting to ambiguity class nuclei, local context generally provides sufficient information for disambiguation (see also section 6).


The 59 erroneous variations point to a total of (a) 134 token errors, (b) 30 of which were detected by ambiguity classes; (c) 17 of these were detected by pairwise ambiguity classes.

can frames be used to group the target words (induction) or to group the contexts (disambiguation)?


Then, the second step considers inserting an arc from 1 to 2.

next, with some effort, we can see that the worst case running time of Adjoining is dominated by the execution of the procedures Fore and Back.


In this case, no violations occur meaning that we can insert the arc.

because k is constant in L k , f (n) is constant and the running time of the algorithm for L k is O(n 5 ) in the worst case.


A detailed discussion is given below in A2 for the grammaticality factors, and in A3-A5 for the similarity factors.

we thus simply adopted the co-occurrence frequency of the phrase and the feature as in (Fujita and Sato, 2008).


As mentioned in Section 1, the aim of the studies reviewed here is to collect paraphrase knowledge.

a5: Finally, the influence of the number of Web snippets was analyzed; no significant difference was observed.


Finally, the conditional probability distribution P (f |p) is estimated as follows: where freq sni (p, r, e) stands for the frequency of the expression e appealing with the phrase p in relation r within the snippets for p. The weight for features P (f ) is estimated using a static corpus based on the following equation: where freq cp (r, e) indicates the frequency of the expression e appearing with something in relation r within the given corpus.

whenever we draw up a document, we always seek the most appropriate expression for conveying our ideas.


This is because pairs are retrieved by using only the BM25.


Obviously, the latter are easy to collect because very non-parallel corpora consist of sets of documents in two different languages from the same period of dates.

we note that the similarity obtained by each hierarchy does not have a fixed range.


A document with Mainichi category "m1" is classified into Reuters category "r12", and a document with Reuters category "r1" is classified into Mainichi category "m21".

as shown in Figure 1, the Reuters document is classified into "Science and Technology," while the Mainichi document is classified into "Space Navigation".


The result decides the preferred entity.

the following rules apply: "increasing comparative" Negative → Negative Opinion "increasing comparative" Positive → Positive Opinion "decreasing comparative" Negative → Positive Opinion "decreasing comparative" Positive → Negative Opinion the first rule says that the combination of an increasing comparative word (e.g., "more") and a negative opinion adjective/adverb (e.g., "awful") implies a negative type 2 comparative.


In order to find whether the combination of C and F indicates a positive or negative opinion, we compute their associations in Pros and in Cons.

pCS using the OSA measure achieves the accuracy of 94.4%, which is much better than the baseline of taking the majority.


Using the semantic dimension, the linguist can specify unification based flat semantic formulae in the sense of (Copestake et al., 2001) i.e., non recursive formulae describing first order formulae with lambda binders replaced by unification variables and where scope may be underspecified.

for instance, when combined with a class C containing a variable X named 6 arg 1 and a variable Y named index subject , the SubjArg1 linking constraint ensures that X and Y are identified.


Finally, we modify the specification of the grammatical functions realisations to import the appropriate semantic class and identify xArg and xF unction nodes.

we then (4.2) show how this specification of the syntax of verbal trees can be enriched with a unification based compositional se-mantics.


In XMG, node names are by default local to a class.

given the space constraints, we concentrate on the verbal trees (trees anchored by verbs).


For a given anaphor, we first select as antecedent according to each anaphora class the candidate with the highest value for P for that class.

the 'perfect' and 'relaxed' scores for C4.5 are pre-  table 4: Performance of the probabilistic model ('perfect') per NP form sented in the last two columns of table 5.


The WordNet Domains package (Magnini and Cavaglia 2000) assigns domains to each sense of the WordNet electronic dictionary.

the effects are measured statistically using linear regression and the resulting line of best fit equation is used to reverse them.


Then a further two words (again originally from set A) are replaced with two more words from another new set (eg from Set C) (AAAAAABBCC), and so on, until the final set of 10 words to be measured consists of 5 pairs of words, each from 5 different sets (AABBCCDDEE).

for each domain a relevant list of words can be extracted.


This reintroduction of truth-values after a forced gap gives rise to a specific set of new regular languages.

in the same way ω can be used to mark a final boundary.


Here, the preparatory phase is making up the whole inner part of the regular expression.

according to the type-coercion paradigm, however, in order to remedy ungrammaticality just one such type-adapted output value would have been sufficient.


2 Attributes in fstructure come in two different types: • Grammatical Functions (GFs) indicate the relationship between the predicate and dependents.

based on the German XLE resources, Cahill et al.


During decoding, the decoder searches the best derivation with the lowest cost by applying SCFG rules.

after extracting features from the training corpus, we use the toolkit implemented by Zhang (2004) to train a MaxEnt RS model for each ambiguous hierarchical LHS.


The scatter-plot in Figure 1 shows the τ with the greatest magnitude.

consider f-score vs. many-to-one, plotted in Figure 3.


For each word w d,,i in document d: where α, β, δ are Dirichlet priors and γ is a Beta To decide the topic of a phrase, we can simply take the first (or last) word's topic or the most common topic in the phrase.

topic models (Blei et al., 2003;Griffiths & Steyvers 2004 are a class of statistical models in which documents are expressed as mixtures of topics, where a topic is a probability distribution over words.


Then, for each word in this document, we randomly select a topic from the distribution, and draw a word from the topic.

lDACOl can decide the length of a phrase dynamically.


We think that patterns are a suitable framework for expressing the structure of N-expressions, because: (a) N-constituents cannot be replaced by any other constituent, so a literal description is appropriate, (b) the order of constituents is not flexible and often fixed.

when a parallel corpus is used, the following two types of constituents need to be considered as C-constituents: (1) the constituent to which there is a semantically corresponding constituent in the English expression, (2) the constituent to which there is no corresponding constituent in English, but deleting  A pattern description language was designed to achieve the following aims: a) patterns can be semi-automatically generated from the result of morphological analysis of a parallel corpus, and b) patterns can be defined according to the degree of generalization.


2-tuple models generate training examples formed as (dependent, candidate).

the criteria for sentence accuracy in this section include all sentences, even if the length is one as Kudo and Matsumoto (2005) did.


• ≺ is an irreflexive and transitive partial order.

then we replace the [...] symbol by a sequence of x symbols.


Yet, often one document describes many transactions and their common attribute values are written only once in the document.

each predicate generated for a given subsequence is a possible description of an entity.


Compared to western couplets, which also consist of two sentences that usually rhyme and have the same number of syllables, Chinese couplets have much stronger constraints.

it also requires the writer to innovatively use extensive knowledge in different disciplines.


The numbers seem a little low, but remember that generating a SS given a FS is a quite difficult job, and even humans cannot do it well in limit time, for example, 5 minute per FS.

finally, a Ranking SVM is applied to rerank the candidates.


Besides the usual local features such as the character-based ones (Xue and Shen, 2003;Ng and Low, 2004), many non-local features related to POSs or words can also be employed to improve performance.

experiments of Ng and Low (2004) on CTB 3.0 also shown the similar trend, where they obtained F-measure 0.952 on segmentation, and 0.919 on Joint S&T.


1: Input: Training examples (xi, yi) 2: α ← 0 3: for t ← 1 .. T do 4: if zi = yi then 7: Collins 2002's perceptron training algorithm were adopted again, to learn a discriminative classifier, mapping from inputs x ∈ X to outputs y ∈ Y .

many theoretically useful features such as higherorder word-or POS-grams can not be utilized efficiently.


This means that our method clusters unknown words using pair-wise constraints.

the result of soft clustering models cannot be directly evaluated by the precision and recall.


Table 1: Four lexico-syntactic patterns, where X and Y are extracted as co-occurring words.

they present an opposite viewpoint from our research.


for example, from a consumers' forum on digital cameras, the underlined parts in Examples (3) and (4) from Section 1 apparently describe the writer's demands, so they are valuable information for such users of demand analysis such as the makers of digital camera.


In Section 4.2 we created a gold-standard dataset with human annotations, however, the number of annotation is not enough to fairly compare the several methods due to the sparseness of the demand targets in the original corpus, and it would be very laborious to prepare a larger annotated dataset.

for example, by investigating demand targets, companies can add new functions to products on the market or plan new services to satisfy customers.


As well as the sentiment expressions leading to evaluations, there are many semantic aspects to be extracted from documents which contain writers' opinions, such as subjectivity (Wiebe and Mihalcea, 2006), comparative sentences (Jindal and Liu, 2006), or predictive expressions (Kim and Hovy, 2007).


(4) I'm waiting for a single-lens reflex less than 30,000 yen to come on the market.

the request in Example (5) does not express the author's demands about any digital camera, but rather it is written for other participants in the forum.


Current techniques for relation extraction include hand-written patterns (Sekimizu et al., 1998), kernel methods (Zelenko et al., 2003, pattern induction methods (Snow et al., 2005), and finite-state automata (Pustejovsky et al., 2002).

such measures as the skew divergence were excluded from the consideration (Lee, 1999).


We collect dependency relations between nouns from automatic parses of the web corpus.

the web corpus, which is used in our evaluation, consists of 759 sentences extracted from the web.


The dnf takes the form: where each S i itself expresses a conjunction of atomic properties.

60 self-reported native or fluent speakers of English, students from various UK universities, did the experiment on the web.


Second, we realise that contextual factors are likely to affect people's interpretive and generative inclinations.

given an intended referent (i.e., the object to be identified) and a set of distractors (i.e., other objects that can be confused with the referent), find a description that allows a hearer to identify its referent uniquely (Dale, 1992).


fine grained clusters (F10C) or coarse-grained ones (F10A) as SPs does not make much difference.

this demonstrates the usefulness of prepositions for the task.


Many words appear in more than one category (at various levels).

the classification was done by comparing the similarity of a target word (i.e.


The acquisition and classification of such region-specific lexical items is an important step toward the larger goal of constructing a Pan-Chinese lexical resource.

the semantic head level is more fine-grained than the subclass level, and is expected to be Head-sg-sg Head-sg-bj more difficult for classification.


In general, up to 85% accuracy was reached with the top 15 candidates for classification at the Cilin subclass level.

there are substantially different lexical items used for lexicalizing common or region-specific concepts.


We would like to investigate which properties of the training examples cause these drops in the uncertainty curve.

we will try to estimate the classifier's performance on unlabeled data.


Moreover, in the update summarization task, the summary length is restricted to about 100 words.

the participants are then required to generate (1) a summary for "A"; (2) an update summary for "B" assuming documents in "A" have already been read; and (3) an update summary for "C" assuming documents in "A" and "B" have already been read.


In this work, we use the sentences in the same sentence set for positive reinforcement and sentences in the different set for negative reinforcement.

focused their efforts on the evolution of nodes and edges in the web graph.


Next, Section 4 reports experiments and evaluation results.

pNR 2 favors the sentences biased to the sentences that are important in the new collection and meanwhile novel to the sentences in the old collection.


For fact-based open ended questions, combining different answers will be useful for reuse of those answers.

case Example: Table 8 presents an example of summarization results of open-type questions.


In order to explain the entailments of a compound expression as a function of the entailments of its parts, we categorize semantic functions according to their projectivity class, a concept which generalizes both Sánchez Valencia's monotonicity classes (upward, downward, and non-monotone) and the nine implication signatures of Nairn et al.

by thresholding a real-valued inference score.


We do not claim natural logic to be a universal solution for NLI.

by incorporating semantic containment, semantic exclusion, and implicativity, the model of natural logic developed in this paper succeeds in explaining a great variety of everyday patterns of inference.


or between language pairs like English-Hindi, English-Urdu, English-French, etc.

to continue with the example in the previous section, the UIt words are deconverted into the Urdu words by the UIt Deconverter using Urdu ↔ UIt transducer in reverse.


Translation of the surface-close languages or inter-dialectal translation can be performed by using mainly transliteration and some lexical translations.

there exist three languages at the border between India and Pakistan: Kashmiri, Punjabi and Sindhi.


The accuracy of the word-word dependencies extracted from the PTB-CFG trees converted from the HPSG treebank was 98.76%.

we assign small scores to the back-off rules as: where is a small constant, P (X → α) is the maximum-likelihood estimate of the PCFG rule probability of X → α in the source treebank, and is the product of 're-labeling probabilities' for the pairs of linked non-terminal symbols in α and β, defined as: where Count(•) represents the number of commonspan node pairs used as the split points.


The Hybrid Algorithm achieved the best accuracy of 95.91%, which corresponds to 15 error cases.

more sophisticated strategies for temporal focus tracking would likely be required in other genres.


First, we check if the temporal preposition since appears immediately in front of a weekday name; if so, the direction of offset is assumed to be backwards; otherwise, the algorithm looks for any verbs in a window of three tokens before and three tokens after the temporal expression.

this approach will not correctly interpret example (10): (10) Still a decision has to made on what, if any, punishment he will face in the wake of that incident Tuesday night.


A search error would indicate that the parsing model could have produced better (more probable) parses if not for heuristics in the search procedure.

there are roughly 330,000 bigrams in WSJ training data and approximately 4.8 million new bigrams in the self-training corpus.


Variables over intermediate f t and modeled q t states are factored into sequences of depth-specific variablesone for each of D levels in the HMM hierarchy: Transition probabilities are then calculated as a product of transition probabilities at each level, using level-specific 'reduce' Θ F and 'shift' Θ Q models: with f D+1 t and q 0 t defined as constants.

in Figure 2, the same repair is shown as it appears in the training data for this system.


The OSCE marking scheme used for this study consists of 5 assessable categories, as follows: APPROACH: the ability of the candidate to communicate with the patient; HISTORY: the ability of the candidate to collect medical history; INTERPRETATION: how well does the candidate interpret his or her investigation in order to formulate an appropriate diagnosis; MANAGEMENT: how well does the candidate formulate a management plan for the diagnosis; COUNSELLING: is the candidate able to give appropriate counselling to the patient.

see section 2 for full details of the examination environment and participants involved.


Recall that APPROACH is the component that assesses language and communication skills.

in encoding features of conversational dominance, we focus on participatory dominance (itakura, 2000), which looks at which speaker contributes most to the dialogue in terms of content.


First, we prefer that the scores not fall substantially during the random walk.

the last set of restarts of MERt had 276,134 hypotheses to consider, a reduction of more than 65% compared to no pruning.


Therefore, for any given inventory, we can define the binary entropy H f (Shannon and Weaver, 1949) for the feature f as If F is the set of all features present in the consonants forming the inventory, then feature entropy F E is the sum of the binary entropies with respect to all the features, that is (3) Since we have assumed that f is an independent random variable, F E is the joint entropy of the system.

we conjecture that the value of α is a function of the societal structure and the cognitive capabilities of human beings.


At each time step a language node establishes the first connection with a consonant node (say C i ) preferentially following the distribution P r(C i ) like the previous models.

we have the synthesized version of PlaNet, which we shall call PlaNet syn henceforth.


The LDA model assumes the bag-of-words model and posits that each document is composed of different topics.

this approach is not plausible for our purposes because of the following two reasons.


The first step is to generate a list of candidate phrases.

this leads to continuously reformulating the search query to discover all the facets of the event.


For example, the NIST sponsored Topic Detection and Tracking (TDT) project, which began in 1998 investigated the development of technologies that could detect novel events in segmented or unsegmented news streams, and track the progression of these event over time (Allan et al., 1998).

we provide a more detailed comparison of the performance of each algorithm using this type as more data was available for it.


The first is the ACE 2005 Multilingual Training Corpus (Walker et al., 2006) that was annotated for 33 different event types.

if a is the number of sentences correctly classified by a system to class i, b is the total number of sentences classified to class i by a system, and c is the total number of human-annotated sentences in class i.


Although this is encouraging, the overall error rate of the language modeling approach is too high to rely on it alone.

the success of this baseline is somewhat dependent on the nature of the event in question.


For example, the combination of P num and C num tests the agreement in number between the anaphoric pronoun and its candidate.

the input of our resolver are the gold mentions annotated in the corpora.


We constructed the training examples in the following way: for each gold anaphora link in the training corpus, we created a positive instance, and negative training instances are created by pairing the pronoun with all of the other markables appearing in a window of w preceding sentences.

the sizes of the data sets used in their experiments were small.


Replac-ing it by wildcards will only produce incorrect corrections or no correction at all because the problem is not related to the lexicon.

it thus exhibits shortcomings of the grammar and can be used to improve it.


This corpora is composed with around 280 000 sentences of 25 or less elements and 4,3 million of words in total.

new POS arise for some forms present in the input.


It is also worth mentioning that phantom tokens, i.e., empty tokens inserted for the analysis of certain elliptical constructions (see section 2), have a labeled precision of 82.4 and a labeled recall of 82.8 (89.2 and 89.6 unlabeled), which is very close to the average accuracy, despite being very infrequent.

the features used are all symbolic and defined in terms of five different node attributes: • POS = part of speech (atomic) • DEP = dependency type Features of the type DEP have a special status in that they are extracted during parsing from the partially built dependency graph and are updated dynamically during parsing.


More precisely, the approach is based on four essential components: • A transition-based deterministic algorithm for building labeled projective dependency graphs in linear time (Nivre, 2003).

it must be remembered that the experiments are based on gold standard input annotation, which probably leads to an overestimation of the value of LEM features.


Finally, the Hellinger distance and kernels squash the variance associated with c through the square root function.

the first is the 90 million word written component of the British National Corpus (Burnard, 1995).


Our corpus includes 13 out of 864 (1.5%) abbreviation definitions in which the abbreviation letters are shuffled.

we have presented a novel approach for recognizing abbreviation definitions.


Additionally, the library supports onthe-fly XSLT-based format conversions that can be easily plugged in via a simple configuration file.

support for XCEs formats is planned as soon as a final release of XCEs is available.


So, a similarity measure in which we are interested is the one which results in high values for pairs of sentences that cover the same facts.

quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study.


The similarity that we use, as described in section 3.2, is such that sentences with the same facts have high similarity values.

subsequently, the pyramid score for a summary is calculated as P = D M ax .


(5) Modification to other constituents: except for the above four types, other CFG rules fall into this type, such as modification to PP, ADVP and PRN etc.

while PP can be removed from the rule ("VP VBZ PP"), the VP should be kept in the rule ("S NP VP").


Nevertheless, the information contained in SPT/CS-SPT ("one of town") may easily lead to their relationship being misclassified as "DISC", which is beyond our expectation.

these strong constraints make their kernel yield high precision but very low recall on the ACE RDC 2003 corpus.


Table 1 evaluates the contributions of different kinds of constituent dependencies to extraction performance on the 7 relation types of the ACE RDC 2004 corpus using the convolution parse tree kernel as depicted in Figure 1.

the key problem of how to represent the structured syntactic parse tree is still partially resolved.


These errors were not revised in the experiment, which damaged the performance.

most of the rules have been incorporated by the utilization of the CRFs model.


We consequently introduce a second kind of rule, which we call a tag transfer rule; as the name suggests, this defines a direct mapping from tags to tags.

this means that concepts may be impossible to represent, or, alternately viewed, that the representation format may conflate concepts which we would prefer to distinguish.


Given the fact that functional tags have some claim to universality, it is reasonable to hope that many tags will map onto themselves.

in the current version of the system, "patient visits doctor" is forced by producing both surface realisations, and defining a generation preference.


We utilize and extend the latter's labeling algorithm.

in the third step of our algorithm we reduce that number.


An isolator occurs when the only hypernym is in a different semantic category to the synset under investigation.

the present study looks for any of the listed faults or potential faults within WordNet 3.0.


In fact, Fellbaum (1998) allows for more than one unique beginner per verb category.

the two hypernyms in each case are in fact hypernyms or synonyms of the respective two meanings, and the grandparent is indeed a common ancestor.


As part of a wider project to build an improved and enriched lexical database using existing resources, in the hope of improving on the performance of WordNet, this study investigates the correctness of the hypernymy/troponymy relations between verbs.

there is scope for introducing more rigour by formalising the definitions along the lines of Jackendoff's (1983Jackendoff's ( , 1990 lexical conceptual structures.


First, cubic complexity of context-free inference is due to O(n 2 ) chart cells and O(n) possible child configurations per cell.

we were quite surprised by these trials, fully expecting these limits to negatively impact accuracy.


Experiments and results are presented and discussed in section 3.


The probability P (n j , c j |CF l , s j , A(s j ) = 1) is called generative probability of a case component and estimated as follows: means the generative probability of a content part n j from a case slot s j in a case frame CF l , and estimated by using the frequency of a case slot example in the automatically constructed case frames.

to deal with this sparseness problem, we generalize the examples of case slots.


Note that only the words, but not the POS tags from the test and development data were used, here.

our tagger was used with a context size of 10.


Experiments training on transformed trees from Sections 2-21 of the WSJ Treebank, evaluating reversed-transformed output sequences from Section 22 (development set) and Section 23 (test set), show an accuracy (F score) of 82.1% and 80.1% respectively.

by adding random variables over environment or discourse referents at each depth and time step.


This decoding process preserves ambiguity by maintaining competing analyses of the entire memory store.

this study will use the trees in the Penn Treebank Wall Street Journal (WSJ) corpus (Marcus et al., 1994) as a data set.


Note that square corners is also the target of a negative sentiment, O4, expressed here by too.

this filters out very small sentences (e.g., "Cool.")


In such scenarios, a positive opinion regarding one choice may imply a negative opinion toward competing choices, and vice versa.

finally, we showed via our machine learning experiments that the presence of opinion frames can be automatically detected.


the frames at the bottom row of the table are non-reinforcing.


Their model relies on semantic frames and extracts as the topic the syntactic constituent at a specific argument position for the given verb or adjective.

we also present a novel method for general-purpose opinion topic identification that, following our new definition, treats the problem as an exercise in topic coreference resolution.


We use the manually annotated data to automatically learn the pairwise classifier.

topic extraction has been effectively substituted with a lexicon look-up and techniques have focused on how to learn or acquire an appropriate lexicon for the task.


Each relation R leads to 2 features that describe for a sense A how many links of that type it has to synsets in the subjective or the objective training set respectively.

still, it is a reasonable assumption that any gloss that contains several subjective words indicates a subjective sense overall.


To avoid prevalence of personalised associations, annotators were told to only annotate polarity for subjective senses, as well as objective senses that carry a strong association likely to be shared by most people at least in Western culture (such as the negative polarity for words referring to diseases and crime).

our mapping from its phrase annotation to sentence annotation might be too coarse-grained as many sentences in the corpus span several clauses containing both opinions and factual description.


The extrinsic label B, which begins a noun phrase, is automatically split into 4 subcategories: wh-determiners (WDT, such as "that") together with wh-pronouns (WP, such as "who"), the determiners (DT, such as "any, an, a"), the personal pronouns (PRP, such as "they, we, he"), and the singular proper nouns (NNP, such as "Nasdaq, Florida") together with the plural nouns (NNS, such as "cities").

latent-dynamics is an intermediate representation between input features and labels, and explicitly modeling this can simplify the problem.


We use the Catvar database to identify verb derivations (Habash and Dorr, 2003).

paths with one variable have more expressive power than paths between two variables, since the combination of two unary paths may generate a binary template that is not a path.


Our first effort to create more accurate temporal relation resolution classifiers given our temporal reasoning engine is to augment the gold training data with new relations from the temporal closure of the relations identified by human annotators.

alternativelly, our main assumption might prove incorrect: temporal closure is not a good indicator of a document's event ordering.


For the hypothetical data in Figure 1, the "OK" sub-corpus contains 90% of the prepositions, and the "Error" sub-corpus contains the remaining 10%.

one disadvantage of ME is that it does not automatically model the interactions among features as some other approaches do, such as support vector machines (Jurafsky and Martin, 2008).


We have shown that head information, which has very little contribution to parsing accuracy as a mere state-split, turns out to have crucial effects within the RR-models.

we next turn to modeling how grammatical relations are realized.


The goal of this experiment is to effectively improve the tagging performance using both these POS ambiguous sentences and the POS unique sentences as the training data.

the total number of resulting distinct features was about 14K.


We show promising results of our method as applied to two types of NLP tasks: a domain adaptation task of a Japanese word segmentation using partial annotations, and a partof-speech tagging task using ambiguous tags in the Penn treebank corpus.

conventional CRF algorithms require fully annotated sentences.


The 80 TOEFL questions yield 320 (80 × 4) word pairs, 80 labeled positive and 240 labeled negative.

with a supervised learning algorithm, we can put our knowledge into the labeling of the feature vectors, instead of putting it directly into the algorithm.


: A CANDY-COMPANY MAY HAVE BARS, and A CAR-COMPANY MAY HAVE CUSTOMERS.

each title in the resultant list is literally a short noun phrase with adjectives as premodifiers.


Also, given that the LANGUAGE attribute does not seem to be assigned perfectly, it would be advisable to examine the misassigned texts, so as to check if they are indeed misassigned or merely mislabeled.

another thing speakers of Dutch seem to do is that they exaggerate their viewpoint, both positively and negatively.


First of all, the annotation of the corpus includes a LANGUAGE attribute, indicating the original language of the text.

finally, as we wanted the classification systems to focus on language use rather than contents, we transformed the tokens in the texts, separately for each target language.


For example, the phrase would be reduced to the ST:  The resolution of pronominal anaphora heavily relies on the syntactic information and relationships between the anaphor and the antecedent candidates, including binding and other constraints, but also context-induced preferences in sub-clauses.

the improvement over the individual and complete tasks suggests that kernel methods are a promising research direction to achieve stateof-the-art coreference resolution systems.


Hence the keyphrase extraction task is conducted separately without interactions for each document.

the co-occurrence relation can express cohesion relationships between words.


The algorithm randomly selects 30 documents as the initial centroids of the 30 clusters and then iteratively assigns all documents to the closest cluster, and recomputes the centroid of each cluster, until the centroids do not change.

the multiple documents within an appropriate cluster context usually have mutual influences and contain useful clues which can help to extract keyphrases from each other.


This paper presents our work on dialogue act classification using cues automatically extracted from a corpus from one domain, and applying these cues directly as a classifier over a new corpus from a different domain.

(2005a) studied the impact of different size training models when classifying SWITCHBOARD data, using models of 4k, 50k and 202k utterances.


This is a significant result, which confirms the idea that cues can be sufficiently general across domains to be used in classification.

an interesting by-product of our approach is the ranked list of cue phrases derived from the source corpus.


(1999) investigate a series of different statistical criteria for use in automatically selecting cue phrases, but we use a criterion of predictivity, described below, which is one that Samuel et al.

whilst the experiment using SWITCH-BOARD corpus derived cues to classify AMITIÉS GE data works well, the same is not true in reverse.


They trained two homogeneous classifiers based on different feature spaces.

rOUGE evaluation results of supervised learning and semi-supervised learning In DUC 2001 dataset, 50, 100, 200 and 400-word summaries are provided to evaluate summaries with different length.


We manually built an in-domain English-French dictionary according to the in-domain bilingual corpus, which includes 26,821 entries.

we com out-of-domain LM in two methods: rimental results indicate that linear interpolation outperforms log-linear interpolation (17.16 vs. 16.20).


And even if such dictionaries are not available, it is easier to compile one than to build a bilingual corpus.

the model used to translate the corpus is that one created by "Europarl+Dic" i at transductive learning significantly improves translation quality, achieving an absolute improvement of 0.58 BLEU score (from 28.22 to 28.80).


Conversely, a loose constraint means that annotations are too generic and have less knowledge incorporated.

in annotated lexical rules, the three elements are combined together and then attached to x as an annotation unit.


Note that for efficiency, we only allow limited modifications to the initial word alignments.

in the process of sampling, we consider a set of alternatives: segmentation c k + along with the product space of relevant alignments in both directions b + k1 , .


Players used the discourse markers "oh" or "wait" 14.5% (25/172) of the time in switching to a picture game.

a large amount of data are required to balance out these differences.


The players get 5 points for the picture game if the correct answer is given in time.

project54 does not provide infrastructure for signaling to the officer a system-initiated switch.


Furthermore, a novel algorithm for term verification is proposed using link analysis to calculate the relevance between term cand ecific corpus to validate their domain specificity.

ac Figure 3 shows that the proposed algorithms achieve similar performance on the legal domain.


Firstly, the delimiters which are mainly functional words (e. g. "在"(at/in), "或"(or)) and general substantive (e.g.

tF-IDF performs relatively well because of the high-quality domain corpus.


Though a cost function between labels can be specified, we gave inserts/deletions and relabellings unit cost.

since the number of participants was small, we simply combined the neighbours with high-similarity judgements for each pivot, yielding an average of 2.45 neighbours per pivot.


Another observation from Table 7 is that WSD_7 identified more erroneous instances when the word entropy exceeded the cut-point, since the over-fitting problem was reduced.

the output of WSD may agree or disagree with the annotators.


Relation extraction was treated as a sequence labeling problem and relational patterns were discovered to boost the performance.

t is the set of training observations (x h , x o ).


We also produced an upper bound using Naive Bayes multinomial (NBm) and Support Vector Machine (SVM) 6 classifiers with the NTU Sentiment Dictionary (Ku et al., 2006) vocabulary items as the feature set.

to do this we assume we can use the lexical item 好 (good) as a gold standard for positive lexical items and compare the pattern of contexts a candidate seed occurs in to the pattern exhibited by the gold standard.


It may also facilitate building sentiment classification systems in other languages since the approach assumes a very small amount of linguistic knowledge: the only language-specific information required is a basic description of the most frequent negated adverbial constructions in the language.

the results would not be comparable since Turney's method would require the additional use of very large text corpus and the manual selection of positive and negative seed words.


From this tree, it is straightforward to obtain 3 It can be shown further that in this region, f shifts up or is unchanged.

in a related study, Zhang and Gildea (2007) analyze the rank of the Synchronous CFG derivation trees needed to parse the same data.


Special interrogative sentence / other interrogative sentence / non-question sentence Chinese sentence can be divided into question sentence and non-question sentence.

cE_dev5 and cE_test are our two test data.


The first one is to test the effect of phrase-ahead reordering model, the result of which is shown in Table 8.

the raw training and test data are all extracted from our training set for translation.


For the MER training, we modified Koehn's MER trainer (Koehn, 2004) for our STSSG-based system.

this is because our study only concerns whether all translational equivalences are linked together by the synchronous parser correctly.


During initial testing of the aligner we found that often lexical links would get higher scores than the non-lexical links, 2 which sometimes resulted in poor lexical links preventing the selection of bona fide non-lexical ones.

the output is again a parallel treebank with unambiguous phrase-structure trees for each language side.


In addition, scores of check-points are also approved to be useful to improve the ranking of MT systems as additional features at sentence level and document level.

for instance, BLEU fails to sufficiently capture the vitality of natural languages: all grams of a sentence are treated equally ignoring their linguistic significance; only consecutive grams are considered ignoring the skipped grams of certain linguistic relations; candidate translation gets acknowledged only if it uses exactly the same lexicon as the reference ignoring the variation in lexical choice.


Too few samples may not be adequate for a reasonable estimate and may result in an incorrect result.

there is a risk to be misleading because how many examples are required for this pregiven separate dataset is an open question in real-world applications, and it can not guarantee that the classifier shows a rise-peak-drop confidence pattern during active learning process.


Our translation direction in this section, Dutch to English, is the opposite of Section 5, for the same reason that we want to use multiple parsers on the target side.

examples of often reoccurring problems include verb positioning (often still sentence-final), positioning of modals in the sentence, etc.


In addition, because the sentences do not always have equal length and we do not want to penalise longer sentences, we also have three features describing the perplexity: e log(P (Tn))/length(Tn) , e log(P (Tr))/length (Tr) , and the difference between these two.

a target-side classifier, when used to identify particularly bad translation candidates, can lead to significant improvements in Bleu score.


two such random variables (Dunning, 1993), and is known to be equal to Mutual Information that is linearly scaled by the size of the corpus (Moore, 2004).

"gear" Method Top 3 candidates Rank baseline jolt, lever, design 284 filtering reverse, gear, lever 2 "pedal" Method Top 3 candidates Rank baseline mj, toyota, action 176 filtering pedal, situation, occasion 1 Table 5: List of translation suggestions using LLR(P) + Manhattan (baseline) and our method (filtering).


We give this estimate a total weight of one observation.

we can assume that, for some content words, we have valid translations in a general dictionary, which enables us to compare the vectors across languages.


In related works that use the PMI (Morin et al., 2007), these probabilities are simply estimated using relative frequencies, as where f (x), f (y) is the document frequency of word x and word y, and f (x, y) is the cooccurrence frequency; n is the number of documents.

we assume that where the parameters for the two beta distributions are set to Prior information related to p(x) and the conditional probability p(x|y) can be incorporated by setting the hyper-parameters of the betadistribtutions.


Section 1); and (ii) even a single sentence could be considered a case of plagiarism, as it transmits a complete idea.

in a society where information in multiple languages is available on the Web, cross-language plagiarism is occurring every day with increasing frequency.


In other Information Retrieval tasks a plethora of corpora is available for experimental and comparison purposes.

a plagiarised sentence is usually not enough to automatically negate the validity of an entire document.


Each of these nodes then discriminates between the first and last element in its list of classes, and so on.

using a graph-based optimisation approach, Goldberg and Zhu demonstrated that the inclusion of unlabeled reviews in the learning process could produce significantly higher prediction accuracy than predictors trained without unlabeled data.


This problem may be addressed by considering SVM regression (SVM-R) (Smola and Schölkopf, 1998), where class labels are assumed to come from a discretisation of a continuous function that maps the feature space to a metric space.

further, our reliance on features supports experimentation with different methods for calculating inter-class similarity (Section 3.2).


In cases when one system was judged to be fluent and the other was not, LMBR+CC was preferred about twice as often as the ML baseline (26.9% to 9.7%).

after applying monolingual coverage constraints to each region, the modified hypothesis space used for MBR search is formed by concatenation using Equation (7).


The input of the learned model is a representation of the free text, its output structured relations.

in most cases, more than one path is returned by the partial dependency parser (which makes no disambiguation decisions) and included in the feature representation.


Methods that are applicable to any unstructured text (not just the text in the Wikipedia) are needed to increase coverage beyond the limited number of instances covered in Wikipedia.

a perfect coverage is not required because the extraction system is only used for training purposes; we only need to find a sufficiently large number of positive training instances and do not require exhaustive labeling of all articles.


To deal with the challenge of finegrained relations, we implement and evaluate both shallow and deep linguistic analysis, focusing on German.

we consider only relations that use a geospatial named entity as second argument.


For non-projective parsing, we use the Non-Projective Approximation Algorithm of McDonald and Pereira (2006).

our faster algorithms may make it feasible to consider further higher order factors.


For Catalan and Chinese, the top results obtained transition-based parsers.

this algorithm therefore has a complexity of O(n 4 ).


As already repeatedly pointed out above, so far we intentionally refrained from optimizing the individual realization stages for specific languages.

for this purpose, the classifier scores each of the minimal edit scripts according to the input, choosing the one with the highest score.


While the availability of such corpora is a condition for deep sentence realization that starts, as is usually the case, from semantic (predicate-argument) structures, we discovered that current annotation schemata do not always favor generation such that additional preprocessing is necessary.

we hope that the overview provided in what follows still suffices to fully assess our proposal.


It starts with an elementary list for each node of the dependency tree.

we cannot contrast our results with any reference work.


Since there are only 100 target words and instances are limited in the Semeval collection, we do not have sufficient samples to estimate the optimal weights for context words.

naïve Bayes (nB) classifiers score classes using the Bayes formula under a feature independence assumption.


The random baseline -assigning a random similarity value to each pair of questions -results in 50% accuracy.

both cos and mcs may miss important structural information.


proper names with partial match, full names and acronyms of organizations, etc.

if a node's degree is below the threshold, the node will be removed.


Hence their system theoretically has access to all possible outcomes making it a potentially global system.

a single document is represented in a hypergraph with basic relational features.


In this work, we assume we are given coreference information, which is available from the ACE annotation.

similarly, let y R,rf be a binary variable which takes on the value of 1 if relation R is labeled with the finegrained label rf .


In order to compare our joint model with previous work, we build several systems: Baseline: There are two independent baseline systems: word sense disambiguation and semantic role labeling.

then we add a formula position(p, a, +po) ⇒ role(p, a, +r) for all possible combinations of position and role relations.


Note that an emotion without causes is labeled as "None".

a cause can also be expressed as a nominal.


ROUGE-L measures the longest common subsequence (LCS) similarity between two documents, rather than simply word overlap so it to some degree reflects fluency.

figure 1 describes the high-level algorithm of our approach.


Instead, we only include the sentences in this segment that contain those keywords.

some of these articles are human-authored translation of the English version with varying degrees of accuracy and completeness, and others are illarranged combinations of excerpts directly adapted from external sources.


Miwa et al., (2009b) executed a set of combinatorial experiments by mixing multiple corpora and pre-sented their results.

when trained on LLL, for example, the range for our system (55.9% ~ 82.1%) is smaller than the others (38.6% ~ 83.2% and 33.3% ~ 76.8%).


So what happens when the representative nodes are very general?

another study that is close to our approach to search space restriction is c-structure pruning (Cahill et al., 2008).